Dynamic model training.    
We adopt common practice in offline MBRL [1]}, [2]} to use an ensemble of Gaussian probabilistic networks \(\widehat{P}(\cdot \,|\,s, a)\)  and \(\hat{r}(s, a)\)  to parameterize the stochastic transition and reward.
We initialize the dynamic model by standard MLE training, and periodically update the model by minimizing Eq. (REF ).
